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1. SAUSA: Securing Access, Usage, and Storage of 3D Point CloudData by a Blockchain-Based Authentication Network

   https://doi.org/10.3390/fi14120354  

   Xu, Ronghua ; Chen, Yu ; Chen, Genshe ; Blasch, Erik ( December 2022 , Future Internet)

   The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which are often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security, Blockchain has great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind the software-defined network (SDN) technology, this paper envisions SAUSA, a Blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud datasets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-inspired point cloud service architecture, which allows for efficient data processing and delivery to satisfy diverse quality-of-service (QoS) requirements. A Blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the Blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability, but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution. 

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2. Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework for Civilian Urban Air Mobility

   https://doi.org/10.3390/app13020755  

   Munir, Md. Shirajum ; Dipro, Sumit Howlader ; Hasan, Kamrul ; Islam, Tariqul ; Shetty, Sachin ( January 2023 , Applied Sciences)

   Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and detecting cyber jamming and spoofing attacks. Then, the developed framework analyzes the conditional dependencies based on the Pearson’s correlation coefficient among the control messages for finding the cause of potential attacks based on the outcome of the AI algorithm. This work considers the UAM attitude control scenario for determining jam and spoofing attacks as a use case to validate the proposed framework with a state-of-the-art UAV attack dataset. The experiment results show the efficacy of the proposed framework in terms of around 99.9% accuracy for jamming and spoofing detection with a decision tree, random forests, and KNN while efficiently finding the root cause of the attack. 

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3. Online Probabilistic Collision Detection for Urban Air Mobility under Data-Driven Uncertainty

   https://doi.org/10.2514/6.2023-2539  

   Wu, Pengcheng ; Chen, Jun ( January 2023 , American Institute of Aeronautics and Astronautics)

   Urban air mobility (UAM) using unmanned aerial vehicles (UAV) is an emerging way of air transportation within metropolitan areas. For the sake of the successful operations of UAM in dynamic and uncertain airspace environments, it is important to provide safe path planning for UAVs. To achieve the path planning with safety assurance, the first step is to detect collisions. Due to uncertainty, especially data-driven uncertainty, it’s impossible to decide deterministically whether a collision occurs between a pair of UAVs. Instead, we are going to evaluate the probability of collision online in this paper for any general data-driven distribution. A sampling method based on kernel density estimator (KDE) is introduced to approximate the data-driven distribution of the uncertainty, and then the probability of collision can be converted to the Riemann sum of KDE values over the domain of the combined safety range. Comprehensive numerical simulations demonstrate the feasibility and eciency of the online evaluation of probabilistic collision for UAM using the proposed algorithm of collision detection. 

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4. liteGAP: Lightweight Group Authentication Protocol for Internet of Drones Systems

   https://doi.org/10.1109/TVT.2023.3335839  

   Pu, Cong ; Warner, Clare ; Choo, Kim-Kwang Raymond ; Lim, Sunho ; Ahmed, Imtiaz ( April 2024 , IEEE Transactions on Vehicular Technology)

   Over the past few years, the synergic usage of unmanned aerial vehicles (later drones) and Internet of Things (IoT) has successfully transformed into the Internet of Drones (IoD) paradigm, where the data of interest is gathered and delivered to the Zone Service Provider (ZSP) by drones for substantial additional analysis. Considering the sensitivity of collected information and the impact of information disclosure, information privacy and security issues should be resolved properly so that the maximum potential of IoD can be realized in the increasingly complex cyber threat environment. Ideally, an authentication and key agreement protocol can be adopted to establish secure communications between drones and the ZSP in an insecure environment. Nevertheless, a large group of drones authenticating with the ZSP simultaneously will lead to a severe authentication signaling congestion, which inevitably degrades the quality of service (QoS) of IoD systems. To properly address the above-mentioned issues, a lightweight group authentication protocol, called liteGAP, is proposed in this paper. liteGAP can achieve the authenticated key establishment between a group of drones and the ZSP concurrently in the IoD environment using lightweight operations such as hash function, bitwise XOR, and physical unclonable function (PUF). We verify liteGAP using AVISPA (a tool for the automatic verification of security protocols) and conduct formal and informal security analysis, proving that liteGAP meets all pre-defined security requirements and withstand various potential cyber attacks. Moreover, we develop an experimental framework and conduct extensive experiments on liteGAP and two benchmark schemes (e.g., GASE and rampIoD). Experimental findings show that liteGAP outperforms its counterparts in terms of computational cost as well as communication overhead. 

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5. Cybersecurity and privacy in smart bioprinting

   https://doi.org/10.1016/j.bprint.2023.e00321  

   Isichei, Joan C ; Khorsandroo, Sajad ; Desai, Salil ( December 2023 , Bioprinting)

   Bioprinting is a versatile technology gaining rapid adoption in healthcare fields such as tissue engineering, regenerative medicine, drug delivery, and surgical planning. Although the current state of the technology is in its infancy, it is envisioned that its evolution will be enabled by the integration of the following technologies: Internet of Things (IoT), Cloud computing, Artificial Intelligence/Machine Learning (AI/ML), NextGen Networks, and Blockchain. The product of this integration will eventually be a smart bioprinting ecosystem. This paper presents the smart bioprinting ecosystem as a multilayered architecture and reviews the cyber security challenges, vulnerabilities, and threats in every layer. Furthermore, the paper presents privacy preservation solutions and provides a purview of the open research challenges in the smart bioprinting ecosystem. 

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